Cranial remodeling device database

ABSTRACT

Databases and a system for operating on such databases are described in which data representative of first head shapes and corresponding modified head shapes and corresponding cranial remodeling devices are provided.

RELATED APPLICATIONS

This application is a continuation-in-part of copending U.S. applicationSer. No. 10/385,307 filed Mar. 10, 2003, for Three-Dimensional ImageCapture System by T. Littlefield and J. Pomatto and assigned to a commonassignee. The following related patent applications are being filed oneven date herewith and are assigned to a common assignee: U.S. patentapplication Ser. No. 10/753,012, Method And Apparatus For ProducingThree Dimensional Shapes by T. Littlefield, J. Pomatto and G. Kechter:U.S. patent application Ser. No. 10/753,118, Automatic Selection ofCranial Remodeling Device Configuration by T. Littlefield and J.Pomatto: U.S. patent application Ser. No. 10/753,006, AutomaticSelection of Cranial Remodeling Device Trim Lines by J. Pomatto and T.Littlefield: and U.S. patent application Ser. No. 10/752,800, CranialRemodeling Device Manufacturing System by T. Littlefield, and J.Pomatto. The disclosures of the above-identified applications areincorporated herein.

FIELD OF THE INVENTION

This invention pertains to cranial remodeling devices, in general, andto a database for use in a cranial remodeling device fabrication system,in particular.

BACKGROUND OF THE INVENTION

Cranial remodeling is utilized to correct for deformities in the headshapes of infants. Prior to the development of the Dynamic OrthoticCranioplasty^(SM) method of cranial remodeling by Cranial Technologies,Inc, the assignee of the present invention, the only viable approach forcorrection of cranial deformities was surgical correction of the shapeof the cranium. Dynamic Orthotic Cranioplasty^(SM) utilizes a treatmentprogram in which a cranial remodeling band is custom produced for eachinfant to be treated. The band has an internal shape that produces thedesired shape of the infant's cranium.

In the past, a cranial remodeling device or band was produced by firstobtaining a full size and accurate model of the infants actual headshape. This first model or shape was then modified to produce a secondor desired head shape. The second or desired head shape is used to formthe cranial remodeling band for the infant. The first shape wasoriginally produced as a cast of the infant's head. The second shape wassimilarly produced as a cast of the head and manually modified to formthe desired shape.

Various arrangements have been considered in the past to automate theprocess of producing cranial remodeling devices. In some of the priorarrangements a scanner is utilized to obtain three dimensional data ofan infant's head. Such arrangements have the disadvantage in thatscanners cannot obtain instantaneous capture of data of the entirety ofan infant's head. In addition, it has been proposed to utilize expertsystems to operate on scanned data to produce an image of a modifiedhead shape from which a cranial remodeling device may be fabricated.However, because each head shape is unique, even the use of an expertsystem may not present an optimized solution to developing modifiedshapes suitable for producing a cranial remodeling device.

Various systems are known for the capturing of images of objectsincluding live objects. One category of such systems typically utilizesa scanning technology with lasers or other beam emitting sources. Thedifficulty with systems of this type is that to scan a three-dimensionalobject, the scan times limit use of the systems to stationary objects.

A second category of image captures systems utilizes triangulatedcameras with or without projection of structured light patterns on theobject. However, these systems typically are arranged to capture athree-dimensional image of only a portion of the object. Typically suchsystems also are used only with stationary objects.

It is highly desirable to provide an image capturing system that willcapture three-dimensional images of objects that are not stationary, butwhich may move. It is also desirable that the three-dimensional imagehas high resolution and high accuracy. It is particularly desirable thatthe three-dimensional image captures the totality of the object.

It is particularly desirable to provide an image capturing system thatwill have the ability to capture an accurate three-dimensional image ofan infant's head. Capturing of such an image has not been possible withprior image capturing systems for a variety of reasons, one of whichbeing that infants are not stationary for the times that prior systemsrequire to scan or capture the data necessary to produce athree-dimensional image. Another reason is that prior systems could onlyacquire a partial three-dimensional imager portion. The need for such asystem is for producing cranial remodeling bands is great.

Prior to the present invention, the process by which a cranialremodeling band is fabricated required obtaining a negative or ‘cast’impression of the child's head. The cast is obtained by first pulling acotton stockinet over the child's head, and then casting the head withquick setting, low temperature plaster.

The casting technique takes approximately 7 to 10 minutes. After theinitial casting, a plaster model or cast of the infant's head is madeand is used for the fabrication of the cranial remodeling band.

It is highly desirable to simplify the process by utilizing digitizationtechniques to produce useful digital three-dimensional images of theentire head. We undertook an exhaustive search to identify and evaluatedifferent digitization techniques. Numerous laser scanning, structuredlight, Moire, and triangulated CCD camera systems were evaluated andrejected as inadequate for one reason or another.

Prior digitization techniques and systems fail to recognize theparticular unique challenges and requirements necessary for a system forthe production of digital images of infant heads. The infant patientsrange in age from three to eighteen months of age. The younger infantsare not able to follow verbal instructions and are not able todemonstrate head control while the older infants are difficult tocontrol for more than a brief moment of time. A wide variety of headconfigurations, skin tone, and hair configurations also needed to becaptured. A digitization system must acquire the image in a fraction ofa second, i.e., substantially instantaneously, so that the child wouldnot need to be restrained during image capture, and so that movementduring image acquisition would not affect the data. The system datacapture must be repeatable, accurate and safe for regular repeated use.In addition, to be used in a clinical setting the system must be robust,easy to use, and easy to calibrate and maintain without the need forhiring additional technical staff to run the equipment. Imageacquisition, processing, and viewing of the data must be performed insubstantially real time in order to ensure that no data was missingbefore allowing the patient to leave the office.

Numerous existing digitization techniques were evaluated. Laser scanningmethods have the disadvantage of the long time, typically 14–20 seconds,that is required to scan an object. Because of the long time, an infantbeing scanned would have to be restrained in a specific orientation forthe scan time. Recent advances in laser scanning have produced scansystems that can perform a scan in 1–2 seconds. However even this scanrate is too slow for an unrestrained infant. The use of lasers alsoraises concerns regarding their appropriateness and safety for use withan infant population. While many prior digitization systems use ‘eyesafe’ lasers, the use of protective goggles is still frequentlyrecommended.

Structured-light Moire and phase-shifted Moire systems used in certain3D imaging systems are difficult to calibrate, are costly, and arerelatively slow and therefore are not suitable for use in obtainingimages of infants. In addition these systems are incapable of capturingthe entirety of an object in one time instant.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are notparticularly useful for the present application simply due to size,expense and concerns regarding radiation and the need to anesthetize theinfant.

Prior systems that rely solely on triangulation of digital camerasproved to have insufficient accuracies, particularly as the object beingimaged varied in shape and size from a calibration standard.

Structured light systems that combined triangulated digital cameras witha projected grid or line pattern can capture only one surface at a timebecause the grids projected by multiple projectors interfered with eachother resulting in a loss of data. In addition, the images captured bythis structured light systems need to be fit together like athree-dimensional jigsaw puzzle, and required that markers be placed onthe subject in order to facilitate this registration process.

SUMMARY OF THE INVENTION

In accordance with the principles of the invention a database fortraining a neural network is provided that comprises a first pluralityof first sets of captured data. Each first set comprises capture datafor a corresponding one first head shape.

In accordance with one aspect of the invention, the database alsoincludes a second plurality of second sets of captured data. Each secondset comprises capture data for a modified head shape for a correspondingfirst head shapes.

In accordance with another aspect of the invention the databasecomprises a third plurality of third sets of data, each third setcomprising cranial remodeling device information for a corresponding oneof said first head shapes. Each third set of data comprises at least oneof cranial remodeling device type, cranial remodeling device style,cranial remodeling device configuration features and cranial remodelingdevice trim lines.

In accordance with other aspects of the invention, each first set ofcapture data is obtained from a digitally captured image of a first headshape and each said second set of captured data is obtained from acaptured image of a corresponding modified head shape.

A system in accordance with the invention comprises a database of afirst plurality of first sets of capture data and a processor foroperating on the first plurality of sets of capture data. Each saidfirst set comprises capture data for a corresponding one first headshape.

In accordance with one aspect of the invention, the system databasecomprises a second plurality of second sets of capture data and theprocessor operates on the second plurality of second sets of capturedata. Each said second set of capture data comprises capture data for amodified head shape for a corresponding one of said first head shapes.

In accordance with another aspect of the invention, the system databasecomprises a third plurality of third sets of data. Each third set ofdata comprises cranial remodeling device information for a correspondingone of said first head shapes. The processor operates on the thirdpluralities of sets of data in conjunction with said the firstpluralities of first data.

BRIEF DESCRIPTION OF THE DRAWING

The invention will be better understood from a reading of the followingdetailed description of embodiments of the invention taken inconjunction with the drawing figures in which like reference designatorsare used to identify like elements, and in which:

FIG. 1 is a block diagram of an image capture system in accordance withthe invention;

FIG. 2 is a top view of a portion of the image capture system of FIG. 1;

FIG. 3 is a cross-section take along lines 3—3 of the image capturesystem portion of FIG. 2;

FIG. 4 is a representation of a random infrared image projected onto anobject for which an image is to be captured;

FIG. 5 is a top view of the image-capturing portion of a secondembodiment of a portion of an image in accordance with the invention;

FIG. 6 is a planar view of an image-capturing module utilized in theimage-capturing portion shown in FIG. 5;

FIG. 7 is a flow diagram of a calibration operation of a system inaccordance with the invention;

FIG. 8 is a flow diagram of operation of a system in accordance with theinvention;

FIG. 9 is a detailed flow diagram of a portion of the flow diagram ofFIG. 8;

FIG. 10 illustrates steps in a method in accordance with the principlesof the invention;

FIG. 11 is a block diagram of a system in accordance with the principlesof the invention;

FIG. 12 illustrates use of Principal Components Analysis and neuralnetworks in the illustrative embodiment of the invention;

FIG. 13 illustrates steps in accordance with another aspect of theinvention;

FIG. 14 is table;

FIG. 15 represents a neural network;

FIG. 16 is a graph;

FIG. 17 is a block diagram of a system in accordance with the principlesof the invention;

FIG. 18 is a block diagram of another system in accordance with theprinciples of the invention;

FIG. 19 illustrates training a neural network in accordance with anotheraspect of the invention;

FIG. 20 illustrates training a neural network in accordance with yetanother aspect of the invention;

FIG. 21 illustrates a portion of the method utilized in the system ofFIG. 18;

FIG. 22 illustrates other aspects of the method utilized in the systemof FIG. 18;

FIG. 23 illustrates a cast of a modified head with trim lines for acranial remodeling band marked shown thereon;

FIG. 24 shows the trim lines of FIG. 22 without the cast; and

FIG. 25 illustrates a data base format in accordance with the principlesof the invention.

DETAILED DESCRIPTION

Turning now to FIG. 1, a block diagram of an image capture system ordigitizer 100 is shown in block diagram form. System 100 includes aplurality of image capturing apparatus 101. Each image capturingapparatus is operable such that a three-dimensional image is capturedfor a surface portion of an object that is disposed within the field ofview of the image capturing apparatus.

The image capturing apparatus 101 are all coupled to and controlled byprocessing apparatus 105 via a bus 107. In addition processing apparatus105 has associated with it program memory 109 and data memory 111. Itwill be understood by those skilled in the art that processing apparatus105 may include one or more processors that are commercially availablefrom a wide variety of sources, such as the Intel Pentium 4 or Itaniumchip based processors. Program memory 109 and data memory 111 may be thesame memory, or each may comprise a plurality of memory units.

Program memory 109 includes an image-processing algorithm that isutilized to process digitized three-dimensional images of surfaceportions provided by image capturing apparatus 101 to produce adigitized image of the entirety of an object.

In operation, processor apparatus 105 controls image capture apparatus101 such that all of image capture apparatus 101 are simultaneouslyoperated to capture digitized first images of corresponding surfaceportions of an object. The digitized first images are uploaded into datamemory 111 under control of processor apparatus 105.

Processor apparatus 105 operates on the digitized first images stored inmemory 111 in accordance with the first algorithm stored in memory 109to produce a composite three-dimensional digitized image from all of thefirst digitized images. The composite three-dimensional digital image isstored in memory 111 by processor 105. A display 113 coupled toprocessor apparatus 105 may be used to display the three-dimensionalcomposite image of the object.

The plurality of image capturing apparatus 101 are arranged to define aspace 200 within which a three-dimensional image is captured of anobject 201. As shown in FIGS. 2 and 3 the image capturing apparatus 101are arranged to define a space 200 in the shape of a hemisphere.Although the illustrative embodiment defines a hemispherical shape, itwill be understood by those skilled in the art that the defined spacemay be of a different configuration. It should also be apparent to thoseskilled in the art that the principles of the invention are not limitedto the positioning of image capturing apparatus to any particular shapeobject 201. For certain objects 201, the image capturing apparatus maydefine a full sphere. In other implementations, the image capturingapparatus may define a space that is elongated in one or moredirections. It will also be apparent to those skilled in the art thatthe size of the space 200 will be determined by the characteristics ofthe plurality of image capturing apparatus.

The number and positioning of image capturing apparatus 101 are selectedto achieve a predetermined accuracy and resolution. The image capturespeed of the image capturing apparatus 101 is selected to provide a“stop-action” image of the object 201. Thus, for example, conventionalphotographic speeds may be used to determine the top speed of an object201 that moves within the space 200. To the extent that an object 201extends outside of space 200, that portion 201A of object 201 that iswithin space 200 will be image captured such that the entirety of thatportion 201A that is within space 200 will captured as a digitizedthree-dimensional image.

In the illustrative embodiment of the invention, each image capturingapparatus 101 includes a plurality of digital cameras 102 such as CCD(charge coupled device) cameras 102 and a projector 104. Each CCD camera102 is a high-resolution type camera of a type that is commerciallyavailable. Each projector 104 projects a pattern onto the object tofacilitate processing of the images captured by the plurality of digitalcameras 102 within an image capturing apparatus 101 into athree-dimensional image of a corresponding portion of the object 201.Projector 104 projects a random infrared pattern 401 as shown in FIG. 4onto the object 201 that permits an algorithm to easily utilizetriangulation to generate a digitized three-dimensional representationof the corresponding portion of object 201.

The CCD cameras 102 and projectors 104 may be supported on one or moresupports such as the representative supports or support members 301, 303shown in FIG. 3.

A particularly useful application of digitizer 100 is for use incapturing three-dimensional images of the totality of an infant's head.Producing a three-dimensional image of an infant is particularlydifficult because infants do not remain motionless. Furthermore themotion that an infant may make is somewhat unpredictable. The infant maymove his or her head in one direction while tilting and rotating it. Themotion may be smooth or it may be jerky. The infant may move his head inone direction while rotating it in the opposite direction. It thereforeis important that the system operate at a speed to capture the entiretyof the infant's head in one instant. To provide a digitizer whichutilizes a safe and noninvasive method of obtaining a 3D model of aninfant's cranium, technological challenges had to be overcome that werenot immediately evident during the initial stages of development. To beuseful in a clinical setting, a digitizer must be fast, safe, accurate,repeatable, quiet, capture all skin tones, be impervious to motion, andnot require the child to be restrained in a specific orientation. To beuseful, the digitizer captures a 360° image which includes the face, topof the head, and lower occiput/neck region. A photographic image of thechild is acquired and can be seamlessly overlaid on thethree-dimensional display of the head to guarantee patientidentification. The digitized model is processed and visualized withinminutes to ensure that no data are missing before allowing the patientto leave the office. Calibration and operation of digitizer 100 issimple, fast, and robust enough to handle normal clinical operation.

Turning now to FIG. 5, one embodiment of digitizer 100 that isparticularly useful with infant head image capture comprises 18triangulated digital cameras 102. Cameras 102 are arranged onto threesupports or modules 501. Six cameras 102 are located in each module 501.Modules 501 are arranged in an equilateral triangle arrangement witheach module 501 located at a vertex. Twelve of the triangulated cameras102 are used to obtain digital image information regarding thethree-dimensional shape of the infant's head 201. The remaining sixcameras 102 capture digital photographs (i.e. texture data) of thechild. A single projector 104 is located in each of the three modules501, and projects a random infrared speckle pattern such as shown inFIG. 4 onto the child 201 at the moment the image is taken. This patterncannot be seen by the operator or the child, but is visible to the 12cameras 102 that obtain the digital shape information.

It is important that the digitizer is calibrated so that the digitaldata accurately represents the object or infant having its imagecaptured. Turning to FIG. 7, calibration is accomplished by placing acalibration object into the center of the digitizer at step 701 and thenoperating all of cameras 102 simultaneously with projectors 104 tosimultaneously capture 12 images of the object at step 703. At step 705,using the 12 images, along with information about the calibrationstandard itself, the precise location and orientation of each digitalcamera 102 with respect to one another is determined. Data regardingeach of the camera's focal lengths obtained at step 707, and lensaberration information obtained at step 709 are recorded with thelocation and orientation data are recorded at step 711 in a calibrationfile. This calibration file is used later to reconstruct a 3D image ofthe child from 12 separate digital images.

To acquire the infant's image, a system operator first enters thepatient information into the digitizer 100 as indicated at step 801 ofFIG. 8. The infant is placed into position as indicated at step 803.Both the child 201 and parent are located in the center of theequilateral triangle with the infant sitting on an adjustable, rotatingstool. The infant 201 is supported by the parent, who may remain in thesystem while the child is digitized. The infant's head is not restrainedand may move in motion having pivotal, rotational and translationcomponents. When the parent and infant are in position the systemoperator actuates digitizer 100 to capture and simultaneously record 18images of the child at step 805. Within two and half minutes, imagesfrom the 12 shape cameras are reconstructed into a 360° digital modelusing the previously recorded calibration data. Texture data (i.e.digital photographs) are automatically overlaid on the model, althoughthe data may be viewed with or without this information. (FIGS. 3–6)Processing the 12 images into a single model can either be doneimmediately following the acquisition, or several images can be acquiredand processed at a later time. Preferably the image is displayed asindicated at step 807 and the image capture is verified at step 809. Theimage data of the obtained image is stored at step 811. If the imageobtained is not acceptable, new images may be captured, displayed andviewed.

Turning now to FIG. 9, the operation of digitizer 100 in capturing animage is shown in a more detailed flow diagram. At step 901, imagecapture is initiated. Simultaneously, all projectors 104 are actuated atstep 903 and all cameras 102 are operated at step 904. The resultingdigital images are downloaded from all of cameras 102 to processor 105at step 907 and stored in memory 111 at step 909. The data from cameras102 in a triangulation pair are processed in accordance with a firstalgorithm in a program module from memory 109 at step 911 to produceintermediate three-dimensional digital images of corresponding portionsof the object or infant's head 201. The intermediate three-dimensionaldigital images are stored in memory 111 at step 913. Processor 105 thenprocesses the intermediate three-dimensional images at step 915 inaccordance with a second algorithm in a program module from memory 109to produce a complete three-dimensional digital image file for the wholeor entire object that is within space 200 or the infant's whole orentire head 201 within space 200. Processor 105 stores the entirethree-dimensional image file in memory 111 for later use.

Accuracy is often reported as a ‘mean’ or ‘average’ difference betweenthe surfaces, however in this situation reporting an average isinaccurate because the surface created from the new data set may havecomponents that lay both above (+) and below (−) the reference surface.These positive and negative values offset each other resulting in a meanvalue around zero. In situations where this cancellation can occur, itis necessary to report the mean difference as a Root Mean Square (RMS).The root mean square statistic reports typical magnitudes of deviationswithout regard for positive or negative values.

By using a best-fit analysis type algorithm to analyze the illustrativedigitizer, the RMS mean deviation between the surfaces was calculated tobe+/−0.236 mm, with over 95% of the data clearly falling within +/−0.5mm.

A hazard analysis performed on the system of the invention demonstratesthat, system 100 is safe. Digitizer 100 will not cause retinalblue-light or infrared eye injuries.

One advantage of digitizer 100 is that the image acquisition is fastenough so that motion of the infant does not present a problem for imagecapture, or affect the accuracy of the data acquired. If the image couldnot be captured ‘instantaneously’ it would be necessary to fixture orrestrain the child in one position in order to ensure there would be nomotion artifacts in the data.

Capture of all 18 images (12 shape, 6 texture) is accomplished throughutilization of an interface 103 in FIG. 1 that functions single framegrabber circuit board. At image capture time processor 105 generates asignal via interface 103 that is sent out to all cameras 102 tosimultaneously record the digital images for processing. Each camera 102records a digital image at a speed of 1/125^(th) of a second (0.008seconds). This nearly instantaneous capture has allowed us to capturedigitized images of infants in motion. The symmetrical placement of thecameras around the periphery also ensures that the child's specificorientation and position within the space 200 is not a factor.

Post-processing of intermediate images into a single digital model isdone quickly so that the complete image can be reviewed before allowingthe patient to leave the office. In an illustrative embodiment of thesystem the complete image may be produced in less than three minutes

Once processed, the data may be viewed in a variety of formats thatinclude point cloud, wire frame, surface, and texture. As the nameimplies, the image presented as a point cloud consists of hundreds ofthousands of independent single points of data. A wire frame, sometimesreferred to as a polygon or triangulated mesh, connects three individualdata points into a single polygon with each data point being referred toas a vertex. A wire frame is the first step in viewing the individualdata points as one continuous connected ‘surface’. Once connected as aseries of polygons, mathematical algorithms are applied to convert thefaceted, polygonized surface into a smooth continuous surface upon whichmore complex measurements and mathematical analyses can be performed.While point cloud, wire frame and surface rendering are the most commonmethods for viewing digital data, it is also possible to obtain textureinformation which is seamlessly overlaid on the model. Texture data isoverlaid onto the digital image to ensure proper patient identification.

The projection of a random infrared pattern by projectors 104, ratherthan a grid or line pattern, overcomes problems with interference andenables digital capture of the entire infant head or object 201 in asingle shot. This includes a 360° image including the face, top of thehead, and neck/occipital region all acquired within 0.008 seconds.Digitizer 100 is safe, impervious to motion, does not require the infantto be sedated or restrained, and images can be viewed within 2–3 minutesof acquisition. The digital data can be exported to create physicalmodels using stereo lithography or carved on a 5-axis milling machine.Quantitative data (linear and surface measurements, curvature, andvolumes) can also be obtained directly from the digital data.

The three-dimensional images are stored in memory 111 of digitizer 100as shown in FIG. 1. A sequence of three-dimensional images may becaptured and stored in memory 111 for later playback. Thethree-dimensional images may be sequentially displayed to produce athree-dimensional movie of the infant or object in motion. A particularfeature is that since each three-dimensional image is taken of theentirety of the infant's head or object, the view of the image onplayback may be changed to observe different portions of the infant'shead or object as it moves. The view may be taken from any point on theexterior of the image capture space defined by the digital cameras.

Turning now to FIG. 10, steps 1001 and 1003 are steps that were utilizedin the past to produce a custom cranial remodeling device or band for aninfant with a deformed head. A positive life size cast is made of aninfant's head or a first shape as indicated at 1001. A correspondingmodified cast or second shape is then made from which a cranialremodeling band is produced at step 1003. In the past, a cranialremodeling band for the infant is produced by forming the band on thesecond cast which represents a modified head shape. A library ofhundreds of infant head casts and corresponding modified casts has beenmaintained at the assignee of the present invention and this library ofactual head casts and the corresponding modified casts is believed to bea unique resource. It is this unique resource that is utilized toprovide databases for developing the method and apparatus of the presentinvention.

An additional unique resource is that databases of additionalinformation corresponding to each infant have been developed by theassignee of the present invention. That database includes informationthat identifies the type of cranial remodeling device for each infanthead shape as well as the style of the cranial remodeling device andfeatures selected for incorporation into the cranial remodeling deviceto provide for appropriate suspension and correction of the deformity.Still further, each cranial remodeling device has trim lines that areuniquely cut so as to provide for appropriate suspension andfunctionality as well as appearance. A further database developed by theassignee of the present invention has trim line data for each cranialremodeling device that has been previously fabricated corresponding tothe casts for unmodified heads.

In a first embodiment of the invention, shown as system 1200 in FIG. 11,the databases 1203, 1205 of unmodified shapes and corresponding modifiedshapes are used by a computer 1201 to train a neural network 1300.

In accordance with one aspect of the invention, each unmodified or firsthead shape is digitally captured at step 1005 as shown in FIG. 10 by adigitizer 100 shown in FIG. 11, to produce first digital data at step1007 to provide a complete three dimensional representation of theentirety of a head including the top portion. The first digital data isstored in database 1203 at step 1009. Each corresponding modified orsecond head shape is digitally captured by digitizer 100 at step 1011 toproduce second digital data at step 1013. The second digital data isstored in database 1205 at step 1015. One to one correspondence isprovided between each first digital data and the corresponding seconddigital data as indicated at step 1017. The correspondence betweendigital data for first and corresponding second head shapes ismaintained by utilizing any one of several known arrangements formaintaining correspondence.

In accordance with one aspect of the illustrative embodiment the firstand second data stored comprises Cartesian coordinates for a pluralityof points on the surface of the corresponding shape.

In the illustrative embodiment shown in FIG. 10, digitizer 100 utilizesa plurality of digital cameras that are positioned to substantiallysurround the entirety of a cast or a patient's head such that asubstantially instantaneous capture of the cast or of the infant's headis obtained. As used herein, the term “digitizer” is utilized toidentify a data capture system that produces digital data thatrepresents the entirety of a cast or a head and which is obtained from asubstantially instantaneous capture.

In accordance with an aspect of the illustrative embodiment of theinvention, neural network 1300 is “trained”, as explained below, so thatcaptured data of a first or unmodified shape 1301 shown in FIG. 12 whichhas no corresponding second or modified shape is processed by neuralnetwork 1300 to produce a second or modified shape 1303. Morespecifically, principal components analysis (PCA) is utilized inconjunction with neural network 1300. Neural network 1300 is trained byutilizing captured data for first shapes from database 1203 withcorresponding captured data for second shapes from database 1205.

Turning to FIG. 13, operation of system 1200 is shown. At steps 1401 and1403, one or more databases are provided to store data for a pluralityof first or unmodified captured shapes and to store data for a pluralityof corresponding second or modified captured shapes.

The data from each captured first and second image is represented usingthe same number of data points. In addition, all captured images areconsistently aligned with each other.

The captured data for all head shapes represented in the databases 1203,1205 of are aligned in a consistent way. The consistent alignment orimage orientation has two separate aspects: alignment of all capturedmodified images with each other as shown at step 1405; and alignment ofeach unmodified captured image with the corresponding modified capturedimage as shown at step 1407. Alignment of unmodified and modifiedcaptured images ensures that the neural network will consistently applymodifications. Alignment of the modified captured images with oneanother allows PCA to take advantage of the similarities betweendifferent cast shapes.

The casts from which the captured images are obtained do not includefacial details. Typically the face portion is merely a plane. Theposition of the face plane is really a result of deformity. To align theshapes a manually iterative visualization process is utilized. Thisapproach “solved” half of the alignment issue—aligning all of themodified images with each other so that the principal componentsanalysis could take advantage of the similarities between these shapes.

To align each unmodified captured image with its corresponding capturedmodified image, alignment of the face planes is was utilized. For themost part, the face planes represent a portion of the shapes that arenot modified from the unmodified to the modified head shape and provideconsistency. An additional attraction to this approach is that there onthe head actual casts, there is writing on the face planes and thiswriting is visible in the texture photographs that can be overlaid ontothe captured images. Alignment of face planes and writing provides aprecise registration of the unmodified and modified captured images.

An automated approach to this alignment was developed using several ofthe first captured images. The automated alignment works where texturephotographs are well focused and clear. In other cases automatedalignment is supplemented with alignment by selecting “freehand” pointson both the modified and unmodified images using commercially availablesoftware and then using the registration tool of that software. Thisapproach aligned all unmodified captures with modified captures so thatcorrections would be consistently applied.

The capture data for both the unmodified and modified head shapes arenormalized at step 1409 for training the neural network. As part of thenormalization, a scale factor is stored for each for each normalizedhead or shape set.

As indicated at step 1411, PCA is utilized with the aligned shapes todetermine PCA coefficients. Because PCA uses the same set of basisvectors (shapes) to represent each head (only the coefficients in thesummation are changed), each captured image is represented using thesame number of data points. For computational efficiency, the number ofpoints should be as small as possible.

The original digitized data for first and second shapes stored indatabases 1203, 1205 represent each point on a surface using athree-dimensional Cartesian coordinate system. This approach requiresthree numbers per point; the x, y, and z distances relative to anorigin. In representing the head captures, we developed a scheme thatallows us to represent the same information using only one number perdata point. Mathematically the approach combines cylindrical andspherical coordinate systems. It should be noted that to obtain threedimensional representations of an object, a spherical coordinate systemmay be utilized. However, in the embodiment of the invention, the bottomof the shape is actually the neck of the infant, and is not of interest.

Conceptually this approach is similar to a novelty toy known as “a bedof nails.” Pressing a hand or face against a grid of moveable pinspushes the pins out to form a 3D copy on the other side of the toy. Thisapproach can be thought of as a set of moveable pins protruding from acentral body that is shaped like a silo—a cylinder with a hemispherecapped to the top. The pins are fixed in their location, except thatthey can be drawn into or out of the central body. Using this approach,all that is needed to describe a shape is to give the amount that eachpin is extended.

To adequately represent each head shape, a fixed number of approximately5400 data points are utilized. To represent each of the captures using afixed number of data points, the distance that each “pin” protrudes iscomputed. This is easily achieved mathematically by determining thepoint of intersection between a ray pointing along the pin direction andthe polygons provided by data from the infrared imager. A set of such“pins” were selected as a reasonable compromise between accuracy of therepresentation and keeping the number of points to a minimum forefficiency in PCA. The specific set of points was copied from one of thelarger cast captures. This number was adequate for a large head shape,so it would also suffice for smaller ones. A commercially availableprogram used to execute this “interpolation” algorithm requires as inputthe original but aligned capture data and provides as output the set ofapproximately 5400 “pin lengths” that represent the shape.

Using the consistent alignment and the consistent array of data pointsdescribed above, the normalized data for each captured cast wasinterpolated onto this “standard grid.” Computing the covariance matrixproduced an n×n matrix, where “n” is the number of data points. As thename implies, this covariance matrix analyzes the statisticalcorrelations between all of the “pin lengths.” Computing the eigenvaluesand eigenvectors of this large covariance matrix provides PCA basisshapes. The PCA shapes are the eigenvectors associated with the largest64 eigenvalues of the covariance matrix. This approach of computingbasis shapes using the covariance matrix makes optimal use of thecorrelations between all the data points used on a standard grid.

PCA analysis allows cast shapes to be represented using only 64 PCAcoefficients. FIG. 14 sets out the hyper parameters for the 64 PCAcoefficients. To transform the unmodified cast shapes into correctmodified shapes, it is only necessary to modify the 64 PCA coefficients.For this processing task we selected and provide neural network 300 asindicated at step 413 of FIG. 4.

Neural networks are an example of computational tools known as “LearningMachines” and are able to learn any continuous mathematical mapping.

As those skilled in the art will understand, learning machines such asneural networks are distinguished from expert systems, in whichprogrammers are utilized to program a system to perform the samebranching sequences of steps that a human expert would perform. Ineffect, an expert system is an attempt to clone the knowledge base of anexpert, whereas, a neural network is taught to “think” or operate basedupon results that an expert might produce from certain inputs.

FIG. 15 shows a conceptual diagram of a generic neural network 1300. Ata high level, there are three elements of a neural network: the inputs,{acute over (α)}₁–{acute over (α)}_(n), The hidden layer(s) 1603, andthe outputs β₁–β_(n). A neural network 1300 operates on inputs 1601using the hidden layer to produce desired outputs 1605. This is achievedthrough a process called “training.”

Neural network 1300 is constructed of computational neurons eachconnected to others by numbers that simulate strengths of synapticconnections. These numbers are referred to as “weights.”

Training refers to modification of weights used in the neural network sothat the desired processing task is “learned”. Training is achieved byusing PCA coefficients for captured data representative of unmodifiedcasts of infant heads as inputs to the network 1300 and modifyingweights of hidden layers until the output of the neural network matchesPCA coefficients for the captured data representative of correspondingmodified casts. Repeating this training thousands of times over theentire set of data representing the unmodified and correspondingcaptured shapes produces a neural network that achieves the desiredtransformation as well as is statistically possible. In the illustrativeembodiment, several hundred pairs of head casts were utilized to trainneural network 1300 at step 1415.

Testing on additional data from pairs of casts that the neural networkwas not trained with, or “verification testing”, was utilized in theillustrative embodiment to ensure that the neural network 1300 haslearned to produce the appropriate second shape from first shapecaptured data and has not simply “memorized” the training set. Once thistraining of neural network 1300 is complete, as measured by the averageleast squares difference between the PCA coefficients produced by thenetwork and those from the modified cast shapes, the PCA coefficientweights are “frozen” and the network is simply a computer program likeany other computer program and may be loaded onto any appropriatecomputer.

A commercially available software toolbox was used to develop thelearning machine. The particular type of learning machine produced iscalled a Support Vector Machine (SVM), specifically a Least SquaresSupport Vector Machine (LS-SVM). Just like the neural networks describedabove, the SVM “learns” its processing task by modifying “weights”through a “training” process. But in addition to weights, the LS-SVMrequires a user to specify “hyper parameters.” For the radial basisfunction (RBF) type of LS-SVM used in this work, there are two hyperparameters: ã (gamma) and ó (sigma). The relative values of theseparameters control the smoothness and the accuracy of the processingtask. This concept is very similar to using different degree polynomialsin conventional curve fitting.

FIG. 16 shows a curve-fitting task in two dimensions for easyvisualization. Because there are a finite number of data points, (x,y)-pairs, it is always possible to achieve perfect accuracy by selectinga polynomial with a high enough degree. The polynomial will simply passthrough each of the data points and bend as it needs to in between thedata where its performance is not being measured. This approach providesridiculous results in between the data points and is not a desiredresult. One solution to this problem is to require that the curvedefined by the polynomial be smooth i.e., to not have sharp bends. Thissolution is fulfilled in the LS-SVM using ã and ó. Higher values of ócorrespond to smoother curves and higher values of ã produce greateraccuracy on the data set.

An unmodified or first shape represented by captured data is processedutilizing a principal components analysis algorithm. The resulting PCArepresentation is processed by a neural network 1300 to produce a secondor modified PCA representation of a modified shape.

In the system of the illustrative embodiment of the inventioncross-validation was used to choose the hyper-parameters. Data israndomly assigned to four groups. Three of the four groups were used totrain the LS-SVM, and the remaining set was used to measure theperformance in predicting the PCA coefficients for the modified headshapes. In turn, each of the four groups serves as the test set whilethe other three are used for training. The group assignment/division wasrepeated two times, so a total of eight training and test sets wereanalyzed (four groups with two repetitions). This process was repeatedfor a grid of (ã, ó)-pairs ranging from 0–200 on both variables. Therange was investigated using a 70×70 grid of (ã, ó)-pairs, so a total of4900 neural nets were tested for each of the first 38 PCA coefficients.From this computationally intensive assessment, hyper-parameters weredetermined and validated for the first 38 PCA coefficients andextrapolated those results to select hyper-parameters for the remaining26. Further “tuning” of the remaining 26 PCA hyper-parameters isunlikely to produce significant improvement in the final results becausethe first PCA coefficients are the most influential on the solution. Thetable shown in FIG. 14 presents the results for the hyper-parametertuning.

Once hyper parameters were tuned, the LS-SVM models generally producederrors of less than two percent for the PCA coefficients of the modifiedcasts in test sets (during cross-validation). Applying these tunedmodels to head casts that were not part of the cross-validation ortraining sets also generated excellent results.

There is a surprising variability of the head shapes as represented bycast shapes. Modified casts are not simply small changes to a consistent“helmet shape.” Each is uniquely adapted to the unmodified shape that itis intended to correct. Being so strongly coupled to the unmodifiedshapes makes these modified casts surprisingly different from oneanother. Of the several hundred casts that we analyzed, each is unique.

Interpolating the aligned shapes also provided surprises and challenges.The “bed of nails” concept is very effective in reducing the size of thedata sets and providing a consistent representation for PCA. It helpsreduce the number of data sets that would have otherwise been requiredto train a larger neural network. Instead of representing each datapoint by three components, each data point is represented by onecomponent thereby reducing the number of data sets significantly. Byutilizing this approach, the process is speeded up significantly.

Returning to FIG. 13, at step 1415, neural network 1300 is trained asdescribed above. Once neural network 1300 is trained, it is thenutilized to operate on new unmodified heads or shapes to produce amodified or second shape as indicated at step 1417.

Turning now to FIG. 17, a block diagram of a system 1800 in accordancewith the principles of the invention is shown. System 1800 is utilizedto both train a neural network 1300 described above and then to utilizethe trained neural network 1300 to provide usable modified head shapesfrom either casts of deformed head shapes or directly from such aninfant's head.

System 1800 includes a computer 1801 which may any one of a number ofcommercially available computers. Computer 1801 has a display 1823 andan input device 1825 to permit visualization of data and control ofoperation of system 1800.

Direct head image capture is a desirable feature that is provided toeliminate the need to cast the children's head. An image capturingdigitizer 100 is provided that provides substantially instantaneousimage captures of head shapes. The digitized image 1821 a is stored in amemory 1821 by computer 1801. Computer 1801 utilizes a data conversionprogram 1807 to normalize data, store the normalized data in memory 1821and its scaling factor, and to convert the normalized, captured data to“bed of nails” data as described above. Computer 1801 stores themodified, normalized data 1821 b in memory 1821. Computer 1801 utilizingan alignment program 1809 to align modified data 1821 b to an alignmentconsistent with the alignments described above and to store the aligneddata in an unmodified shapes database 1803. Computer 1801 obtains PCAcoefficients and weightings from a database 1813 and utilizes neuralnetwork 1300 and a support vector machine 1817 to operate on the datafor a first shape stored in memory 1821 a to produce data for a modifiedor second shape that is then stored in memory 1821 b. The data for themodified shape stored in memory 1821 b may then be utilized to fabricatea cranial remodeling device or band for the corresponding head.

In another embodiment of the invention, shown in FIG. 18, a system 1900is also “trained” such that in addition to producing a modified headshape that is utilized for fabrication of a cranial remodeling band,system 1900 additionally determines a type and style of the cranialremodeling device or band which is particularly appropriate for thedeformity as well as a configuration for the device or band. The typeand style of device is determined, in part, from the nature and extentof the cranial deformity and/or from the fit and function of the cranialremodeling band to correct the cranial deformity.

The type of remodeling device or band is selected based upon the natureof the deformity. In the illustrative embodiment of the invention, theband types may be classified as Side-Opening, Brachy Band®, or Bi-Cal™.

The DOC Band® or side-opening type is used primarily to treat childrenwith plagiocephaly, or asymmetrical head configurations. It appliesforces in a typically diagonal fashion. A representative side-openingband is described in U.S. Pat. No. 5,094,229 which is incorporatedherein by reference.

The Brachy Band® is used to treat brachycephaly, or deformations wherethe head is too wide, and too short. It applies forces on the lateralprominences and encourages growth of the head in length. This returnsthe head to a more normal cephalic index (length to width ratio). Anexample of a Brachy Band is shown in U.S. Pat. No. Re. 36,583 which isincorporated herein by reference.

The Bi-Cal™ type is used to treated scaphocephaly, or deformations wherethe head is too long, and too narrow. It applies forces on the foreheadand back of the head and encourages growth of the head in width. Thisreturns the head to a more normal cephalic index (length to width ratio)

In the illustrative embodiment of the invention, the style of band ordevice includes: RSO or right side opening cranial remodeling band; WRSOor wide right side opening cranial remodeling band; LSO or left sideopening cranial remodeling band; WLSO or wide left side opening cranialremodeling band.

The configuration of the devices is selected to provide specificfunctional attributes. Examples of such attributes include: suspension;application of corrective forces; and protection. Suspension refers tothose design configuration features that help to maintain the band inits proper orientation so that the corrective forces are applied wherethey need to be. In some cases, the design features themselves are usedto apply corrective forces which could not be achieved without theirinclusion. In some cases, the features are there to protect a surgicalsite. An example of this would be a strut that goes over the top of thehead in the bi-cal band.

Typical features include: use of anterior corners (unilateral orbilateral), posterior corners (unilateral or bilateral), fractionalanterior tops, fractional posterior tops, opposing corners, struts, orvarious combinations of each. It is not possible to genericallycategorize each feature, e.g., anterior corner, ¼ posterior top, etc, asonly used to provide a single function. In some instances, the featuresare multi-functional, for example providing both suspension and acorrective force.

In the illustrative embodiment of the invention, the “features” includestandardized structural configurations which are referred to as: RAC orright anterior corner; LAC or left anterior corner; RPC or rightposterior corner; LPC or left posterior corner; a fractional or noanterior or posterior cap; and a partial or full strut across the top ofband.

The features may be combined in any number of combinations, but the mostcommon would be what is referred to as an “opposing corners”combination. An example of this would be a right side opening band thatalso has both a right anterior corner (RAC) and a left posterior corner(LPC). These features are not for aesthetics, but rather representfunction improvements to the band for both suspension and application ofcorrective forces.

It is difficult, if not impossible, to identify what type of cranialremodeling device or band and its features should be for a patient ifonly information of the corrected shape is provided.

In the past, the type of cranial remodeling device or band, and theadditional features that should be incorporated were determined in thepast during the modification process. Thus when a head cast is beingmodified to produce a modified cast, a determination is made as to boththe necessary style of the band to be used and the features that shouldbe incorporated into the band. Both the selection of the type of deviceor band as well as the features to be incorporated are a function ofboth the original deformity as well as the corrected head shape. Thefeatures selected are not independent.

In system 1900, a database 1901 includes for each unmodified head shapedataset data identifying the type and style of cranial remodeling deviceas well as configuration features.

In system 1900, neural networks 1915 include neural network 1300 trainedto provide modified shape data from unmodified shape data as describedwith respect to system 1800. In addition neural networks 1915 includesneural network 1916 that is trained to select a type and style ofcranial remodeling device and to select a configuration of the cranialremodeling device.

Turning now to FIG. 25, the type, style and configuration data entryformat stored in database 1901 for each head is shown. Each databaseentry includes a reference file number to assist in correlating to thecorresponding head shape in database 1803. Each database entry alsoincludes an entry field for a band type, an entry field for a bandstyle, four entry fields for each of the right and left anterior cornersand right and left posterior corners, a field for identification of afractional anterior top, a field for identification of a fractionalposterior top, and a field for identification of a strut.

The methodology for training the neural networks 1916 is similar to thatused in the first embodiment. Turning now to FIG. 19, data forunmodified head shapes obtained from database 1203 and correspondingtype, style and configuration data for cranial remodeling devices fromdatabase 1901 are utilized to train neural network 1916 such that neuralnetwork 1916 will automatically select the type, style and configurationdata for a cranial remodeling device.

By providing neural networks 1915 trained to generate datarepresentative of a corrected head shape and to select a correspondingcranial remodeling band and features, a highly automated cranialremodeling band fabrication system is provided by system 1900.

System 1900 includes a milling machine 1903 that receives data fromcomputer 1801 and mills a model. Milling machines are commerciallyavailable that will receive digital data from a computer or otherdigital data source and which produce a milled three dimensional object.In system 1900 milling machine 1903 is one such commercially availablemilling machine. In operation system 1900 instantaneously captures threedimensional image data of an infant's head utilizing digitizer 1819.Computer 1801 stores the captured data 1821 a in memory 1821. Computer1801 then utilizes data conversion module 1807 to convert the data intoa “bed of nails” equivalent data and to provide alignment of thecaptured image and to restore the converted and aligned data in memory1821. Computer 1801 utilizes neural network 1300 in conjunction with theconverted and aligned data of the captured image to producecorresponding three dimensional image data for a modified or second headshape. The three dimensional data 1821 b for the modified or second headshape is stored in memory 1821. In addition, neural network 1916 is alsoutilized to select a corresponding cranial remodeling band type, styleand configuration which are likewise stored in memory 1821.

Computer 1801 then utilizes the three dimensional data 1821 b for themodified shape to command and direct milling machine 1903 to produce anaccurate three dimensional model of the modified shape represented bydata 1821 b.

Computer 1801 also retrieves corresponding cranial remodeling band type,style and configuration features which are displayed on display monitor1823 to assist in fabricating a cranial remodeling band for the infantwhose head was digitally captured.

In another embodiment of the invention, once the milling machine 1903has produced a three dimensional representation of a modified head basedupon data provided by computer 1801, a copolymer shell is vacuum formedon the representation of the head. The copolymer shell is then shaped toproduce the particular device type, style and configuration in a furtherdigital controlled machine 1907.

To summarize, an infant having a deformed head that requires treatmentwith a cranial remodeling band may have his or her head shape digitallycaptured by system 1900. System 1900 may be utilized to automaticallyproduce a three dimensional representation of the infant's head shapemodified to produce a cranial remodeling band to correct for the headdeformities. System 1900 further may be operated to automaticallyprovide an operator with information pertaining to the appropriateconfiguration and features to be provided in the cranial remodelingband.

In accordance with yet another aspect of the present invention, inaddition to data relating to the band type, style and configurationfeatures, the data may include data for trim lines for cranialremodeling bands. A neural network 1918 included with the neuralnetworks is trained in the same manner that neural network 1916 istrained with trim line data as illustrated in FIG. 20. By including datafor trim lines, in database 1901A and utilizing neural network 1916 toalso learn the trim lines to be utilized in various cranial remodelingbands, system 1900 produces a cranial remodeling band of an appropriateconfiguration and having appropriate features, and appropriate trimlines all without any significant human intervention.

FIG. 21 illustrates the method of storing cranial remodeling device typestyle and feature data and trim line data. FIG. 21 is similar to theflow diagram of FIG. 10 with the additional step of storing in adatabase cranial remodeling device type, style and configuration featuredata corresponding to first digital data at step 1019. FIG. 21 alsoillustrates the step of storing in a database trim line data for cranialremodeling devices: corresponding to first digital data at step 1021.

Neural network 1916 is thus trained to automatically generate trimlines. Once generated, trim line data may be utilized to actually milltrim lines right onto the copolymer cranial band vacuum formed onto thatthree dimensional representation of a modified head either utilizingmilling machine 1903 or machine 1907. Machine 1907 may be a lasertrimming machine.

FIGS. 23 and 24 illustrate exemplary trim lines for a cranial remodelingband. In FIG. 23 trim lines 2301, 2303 are shown on a modified headshape 2300. To better see the trim lines 2301, 2303, FIG. 23 shows thetrim lines 2301, 2303 for the cranial remodeling band without head shape2300. Trim line 2301 illustrates the lower margin of cranial remodelingdevice or band for head shape 2300 and trim line 2303 illustrates theupper margin of the cranial remodeling band.

FIG. 22 illustrates the method of training system 1900. The method ofFIG. 22 is similar to the method of FIG. 13. At step 2201 a database ofcranial remodeling device type, style and configuration feature data isprovided. At step 2203 a database of trim line data is provided. At step2205 a neural network is trained to select cranial remodeling devicetype, style and configuration features. At step 2207, a neural networkis trained to select trim lines for the cranial remodeling device. Atstep 2209, the trained neural networks are utilized to produce a cranialremodeling device.

It will be appreciated by those skilled in the art that a system 1900 inaccordance with the principles of the invention may be configured toautomate various aspects of producing cranial remodeling devicesautomatically and ranging from automatic production of three dimensionalrepresentations of head shapes modified to shapes that permit theforming of an appropriate cranial remodeling band to automaticproduction of cranial remodeling bands to be utilized in the correctionof head shape abnormalities.

The invention has been described in terms of illustrative embodiments.It will be apparent to those skilled in the art that various changes andmodifications can be made to the illustrative embodiments withoutdeparting from the spirit or scope of the invention. It is intended thatthe invention include all such changes and modifications. It is alsointended that the invention not be limited to the illustrativeembodiments shown and described. It is intended that the invention belimited only by the claims appended hereto.

1. A database for training a neural network, comprising: a firstplurality of first sets of first captured data utilizable to train saidneural network, each said first set comprising first capture data for acorresponding one first head shape: and a second plurality of secondsets of second captured data utilizable to train said neural network,each said second set comprising second capture data for a modified headshape for a corresponding one said first head shape, wherein said headshape is a shape of an infant's head.
 2. A database in accordance withclaim 1, comprising: a third plurality of third sets of data, each saidthird set of data comprising cranial remodeling device information for acorresponding one said first head shape.
 3. A database in accordancewith claim 2, wherein: each said third set of data comprises at leastone of cranial remodeling device type, cranial remodeling device style,cranial remodeling device configuration features and cranial remodelingdevice trim lines.
 4. A database in accordance with claim 1, wherein:each said first set of first capture data is obtained from digitallycaptured images of a first head shape.
 5. A database in accordance withclaim 4, wherein: each said second set of second capture data isobtained from captured images of a corresponding modified head shape. 6.A database in accordance with claim 5, wherein: each said modified headshape comprises a model of a modified head shape.
 7. A database inaccordance with claim 4, wherein: each said first head shape comprises amodel of a first head shape.
 8. A database in accordance with claim 7,wherein: each said model is prepared from a cast of a correspondinginfant head.
 9. A database in accordance with claim 1, wherein: eachsaid first set of first captured data comprises a plurality of Cartesiancoordinates each corresponding to a point on said corresponding onefirst head shape: and each said second set of second captured datacomprises a second plurality of Cartesian coordinates each correspondingto a point on a corresponding one said modified head shape.
 10. Adatabase in accordance with claim 9, wherein: each said first set offirst captured data is obtained from a digitally captured image of afirst head shape.
 11. A database in accordance with claim 10, wherein:each said second set of second captured data is obtained from adigitally captured image of a corresponding modified head shape.
 12. Adatabase for training a neural network, comprising: a first plurality offirst sets of captured data, each said first set comprisingthree-dimensional capture data for a corresponding one first head shape,said three-dimensional capture data utilizable to train said neuralnetwork; and a third plurality of third sets of data, each said thirdset of data comprising cranial remodeling device information for acorresponding one of said first head shapes, said third set of datautilizable to train said neural network wherein said head shape is ashape of an infant's head.
 13. A database in accordance with claim 12,wherein: each said third set of data comprises at least one of cranialremodeling device type, cranial remodeling device style, cranialremodeling device configuration features and cranial remodeling devicetrim lines.
 14. A system, comprising: a database comprising: a firstplurality of first sets of capture data, each said first set comprisingcapture data for a corresponding one first head shape: and a secondplurality of second sets of capture data, each said second setcomprising capture data for a modified head shape for a correspondingone of said first head shapes: and a processor operated to train one ormore neural networks with said first plurality of first sets of capturedata and said second pluralities of second sets of capture data, whereinsaid head shape is a shape of an infant's head.
 15. A system inaccordance with claim 14, comprising: a third plurality of third sets ofdata, each said third set of data comprising cranial remodeling deviceinformation for a corresponding one of said first head shapes: and saidprocessor operating on said third pluralities of sets of data inconjunction with said first pluralities of first data and said secondpluralities of said second sets of data.
 16. A system in accordance withclaim 15, wherein: each said third set of data comprises at least one ofcranial remodeling device type, cranial remodeling device style, cranialremodeling device configuration features and cranial remodeling devicetrim lines.
 17. A system in accordance with claim 14, wherein: each saidfirst set of capture data is obtained from a digitally captured image ofa first head shape.
 18. A system in accordance with claim 17, wherein:each said second set of captured data is obtained from a captured imageof a corresponding modified head shape.
 19. A system in accordance withclaim 14, wherein: said captured data for each said first set ofcaptured data comprises a plurality of Cartesian coordinates eachcorresponding to a point on said corresponding one first head shape; andsaid captured data for each said second set of capture data comprises asecond plurality of Cartesian coordinates each corresponding to a pointof a corresponding one said modified head shape.
 20. A system inaccordance with claim 19, wherein: each said first set of capture datais obtained from a digitally captured image of a first head shape.
 21. Asystem in accordance with claim 20, wherein: each said second set ofcaptured data is obtained from a captured image of a correspondingmodified head shape.
 22. A system, comprising: a database comprising: afirst plurality of first sets of capture data, each said first setcomprising capture data for a corresponding one first head shape; and athird plurality of third sets of data, each said third set of datacomprising cranial remodeling device information for a corresponding oneof said first head shapes; and a processor for operating on said firstplurality of sets of capture data and said third pluralities of sets ofdata, wherein said head shape is a shape of an infant's head.
 23. Asystem in accordance with claim 22, wherein: each said third set of datacomprises at least one of cranial remodeling device type, cranialremodeling device style, cranial remodeling device configurationfeatures and cranial remodeling device trim lines.